The DOI matches its Crossref record; 11 of the 17 references it cites were found in a registry or an index, and 6 are not checked yet; none of the 9 sources its claims cite is checked for support yet.
Climate model genealogy: Generation CMIP5 and how we got there
Open accessAbstract
A new ensemble of climate models is becoming available and provides the basis for climate change projections. Here, we show a first analysis indicating that the models in the new ensemble agree better with observations than those in older ones and that the poorest models have been eliminated. Most models are strongly tied to their predecessors, and some also exchange ideas and code with other models, thus supporting an earlier hypothesis that the models in the new ensemble are neither independent of each other nor independent of the earlier generation. On the basis of one atmosphere model, we show how statistical methods can identify similarities between model versions and complement process understanding in characterizing how and why a model has changed. We argue that the interdependence of models complicates the interpretation of multimodel ensembles but largely goes unnoticed.
Checked 2026-10-06 · 6 references not checked yet
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
0 of 9 cited sources checked · 0 supported · 0 flagged
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“and some also exchange ideas and code with other models”
§ Climate model genealogy: Generation CMIP5 and how we got there > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“Here, we show a first analysis indicating that the models in the new ensemble agree better with observations than those in older ones”
- Also stated in: § 2. Results · plain
“CMIP5 continues the trend of better agreement with observations [Reichler and Kim, 2008], with the mean distance from observations reduced by approximately 20% from CMIP3 to CMIP5.”
§ Climate model genealogy: Generation CMIP5 and how we got there > Abstract
What the sources say
- Not checked yetcites Reichler and Kim, 2008
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 2. Results · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Most models are strongly tied to their predecessors”
§ Climate model genealogy: Generation CMIP5 and how we got there > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“On the basis of one atmosphere model, we show how statistical methods can identify similarities between model versions and complement process understanding in characterizing how and why a model has changed.”
§ Climate model genealogy: Generation CMIP5 and how we got there > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The distance between models from the same institutions is often a factor of three to ten smaller than that to other models.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Sharing of model code occurred in CMIP3 (e.g., the Italian INGV model using an ECHAM atmosphere, or the Norwegian BCCR using the French ARPEGE atmosphere also present in CNRM) but has become more widespread in CMIP5.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The strengths and weaknesses of particular models, as they are evident in the biases in simulating present day climate, are partly passed on to newer model versions and to other models by the exchange of code and ideas.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 3. Conclusion and Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The contribution of internal unforced variability (quantified using multiple segments of a control simulation) is small for this metric”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In general, the CAM models with the most similar physics packages cluster closest together.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The similarity metric is defined from the unperturbed preindustrial model control state and is strongly determined by biases in the present day climatology and seasonal cycle, that is, by model differences rather than initial conditions or forcing.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 10
- The quote only partly states this claim. 1
- Probably not a substantial claim. 7
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“and that the poorest models have been eliminated”
§ Climate model genealogy: Generation CMIP5 and how we got there > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As is clear from the tree shown in Figure 2a, the CAM5 experiments cluster together, with three single perturbation experiments similar to the base CAM5 experiment.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“as shown already by MK11, the different observation and reanalysis data set cluster together (see Figure 1a), and the rankings in Figure 3 are nearly identical irrespective of the chosen data set.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
What the sources say
- Not checked yetcites Masson and Knutti, 2011
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For the projected changes, some models remain close (e.g., those which share a similar atmosphere), whereas other similarities do not persist (e.g., if the new version of a model has a different climate sensitivity).”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The final change in the shallow convection introduces the largest change (last step in panel c), although it brings CAM5 closer to the original CAM4 in some variables (panel b).”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“thus supporting an earlier hypothesis that the models in the new ensemble are neither independent of each other nor independent of the earlier generation”
§ Climate model genealogy: Generation CMIP5 and how we got there > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The results point to a remarkable consistency and robustness in many aspects of simulated present day and future climate, but it also suggests that convergence to reality is slow and radical breakthroughs are hard to achieve.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 3. Conclusion and Discussion
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Confidence is greatest in those aspects of climate change that we understand and can link back to known physical processes and simpler models and concepts (e.g., the global energy balance).”
§ Climate model genealogy: Generation CMIP5 and how we got there > 3. Conclusion and Discussion
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We argue that transparency in the model development process is helpful in understanding model evaluation and projections and contributes to that insight.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 3. Conclusion and Discussion
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Note that all results presented here are only based on model output; they assume no knowledge about the structure, parameterizations, or code of any model.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“None of those choices affect the main conclusions.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Confidence in model projections does not come from the sheer amount of code and data and cannot be demonstrated by repeated verification.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 3. Conclusion and Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
In the paper
“but with the strong caveat that linking model performance metrics to model quality or skill is difficult, subjective, and strongly metric dependent [Gleckler et al., 2008]”
§ Climate model genealogy: Generation CMIP5 and how we got there > 2. Results
What the sources say
- Not checked yetcites Gleckler et al., 2008
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We propose that one reason some models are so similar is because they share common code.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 3. Conclusion and Discussion
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds.
In the paper
“MK11 argued that such similarities result from the fact that models evolve from their ancestors by modification and by exchange of ideas and code with other groups.”
§ Climate model genealogy: Generation CMIP5 and how we got there > 1. Introduction
What the sources say
- Not checked yetcites Masson and Knutti, 2011
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The authors state this as an assumption.
The models in an ensemble are approximately a representative sample of uncertainty in how to best describe the climate system given limited observations, imperfect understanding, and finite computational resources.
“This assumes that the models are approximately a representative sample of our uncertainty in how to best describe the climate system given limited observations, imperfect understanding, and finite computational resources [Knutti, 2008;Yokohata et al., 2012].”
The authors state this as an assumption.
There are not too many similarities among models that would bias the results of an ensemble.
“It also assumes that there are not too many similarities that would bias the results.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Since CMIP5 projections are an incremental advance over CMIP3 data [34–36], this does not limit the relevance of our analysis as a tool for assessing anthropogenic influences on future fire activity.”
From Incorporating Anthropogenic Influences into Fire Probability Models: Effects of Human Activity and Climate Change on Fire Activity in California (2016) · cites this paper as [34]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Overall distance metric is described two different ways
MinorThe methods section says the combined temperature-precipitation distance used for Figure 3 is a mean square, while the Figure 3 caption calls it a root mean square. Taking the square root changes the reported distance values.
“for Figures 1 and 3, the metric now also includes differences in the annual mean climatology and that we use the mean square of the temperature and precipitation distance to define overall similarity”
“The distance metric is calculated as the root mean square of the surface temperature and precipitation distance as in Figure 1”
Citations
Cites
Climate change 2007: the physical science basis
Solomon, S., Qin, D., Manning, M., Marquis, M. C., Averyt, K. B., Tignor, M., Miller, H. L., & Chen, Z. (2007) · Cited by 23,398
An Overview of CMIP5 and the Experiment Design
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052
The use of the multi-model ensemble in probabilistic climate projections
Tebaldi, C., & Knutti, R. (2007) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2007.2076 · Cited by 1,862
Self-citationRobustness and uncertainties in the new CMIP5 climate model projections
Knutti, R., & Sedláček, J. (2013) · Nature Climate Change · doi:10.1038/nclimate1716 · Cited by 1,569
Self-citationPerformance metrics for climate models.
Gleckler, P. J., Taylor, K. E., & Doutriaux, C. (2008) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2007jd008972 · Cited by 1,328
Challenges in combining projections from multiple climate models.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., & Meehl, G. A. (2010) · Journal of Climate · doi:10.1175/2009jcli3361.1 · Cited by 1,270
Self-citationA numerical method for the study of the circulation of the world ocean
Bryan, K. (1969) · Journal of Computational Physics · doi:10.1016/0021-9991(69)90004-7 · Cited by 974
A Numerical Method for the Study of the Circulation of the World Ocean
Bryan, K. (1997) · Journal of Computational Physics · doi:10.1006/jcph.1997.5699 · Cited by 906
How Well Do Coupled Models Simulate Today's Climate?
Reichler, T. J., & Kim, J. (2008) · Bulletin of the American Meteorological Society · doi:10.1175/bams-89-3-303 · Cited by 880
Flagged first, then by citations
Cited by
Sorted by citations
Items 501–600 of 918
Pokorny, S., Stadnyk, T., Lilhare, R., Ali, G. A., Déry, S. J., & Koenig, K. A. (2020) · Water · doi:10.3390/w12102751 · Cited by 16
Bradford, J. B., Andrews, C. M., Robles, M. D., McCauley, L. A., Woolley, T. J., & Marshall, R. M. (2020) · Ecological Applications · doi:10.1002/eap.2238 · Cited by 15
Georgescu, M., Broadbent, A. M., & Balling, R. C. (2021) · International Journal of Climatology · doi:10.1002/joc.7444 · Cited by 16
Sreekumar, E. R., & Nameer, P. O. (2021) · Current Science · doi:10.18520/cs/v121/i10/1335-1342 · Cited by 16
Wehrli, K., Luo, F., Hauser, M., Shiogama, H., Tokuda, D., Kim, H., Coumou, D., May, W., Sager, P. L., Selten, F., et al. (2022) · Earth System Dynamics · doi:10.5194/esd-13-1167-2022 · Cited by 19
Deriving user-informed climate information from climate model ensemble results
Huebener, H., Hoffmann, P., Keuler, K., Pfeifer, S., Ramthun, H., Spekat, A., Steger, C., & Warrach‐Sagi, K. (2017) · Advances in science and research · doi:10.5194/asr-14-261-2017 · Cited by 16
Climate change increases ecogeographical isolation between closely related plants
Duffy, K. J., & Jacquemyn, H. (2018) · Journal of Ecology · doi:10.1111/1365-2745.13032 · Cited by 16
Larger Sensitivity of Arctic Precipitation Phase to Aerosol than Greenhouse Gas Forcing
Pan, S., Dou, T., Lin, L., Yang, J., Zhang, F., Duan, M., Zhao, C., Liao, H., & Xiao, C. (2020) · Geophysical Research Letters · doi:10.1029/2020gl090452 · Cited by 16
Historical, Philosophical, and Sociological Perspectives on Earth System Modeling
Rödder, S., Heymann, M., & Stevens, B. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2020ms002139 · Cited by 15
Climate Information: Towards Transparent Distillation
Jack, C. D., Marsham, J. H., Rowell, D. P., & Jones, R. (2021) · Springer eBooks · doi:10.1007/978-3-030-61160-6_2 · Cited by 19
Tuan, L. Q., Thong, V. D., Sơn, N. T., Tú, V. T., Tuấn, T. A., Lương, N. T., Vy, N. T., Thành, H. T., Huang, J. C., Csorba, G., et al. (2023) · Biodiversity and Conservation · doi:10.1007/s10531-023-02709-5 · Cited by 15
Ren, X., Lunt, D. J., Hendy, E., von der Heydt, A. S., Abe‐Ouchi, A., Otto‐Bliesner, B. L., Williams, C. J. R., Stepanek, C., Guo, C., Chandan, D., et al. (2023) · Climate of the past · doi:10.5194/cp-19-2053-2023 · Cited by 15
Zhao, J., He, S., Fan, K., Wang, H., & Li, F. (2024) · Advances in Atmospheric Sciences · doi:10.1007/s00376-023-2393-2 · Cited by 16
Olschewski, P., & Kunstmann, H. G. (2024) · Frontiers in Climate · doi:10.3389/fclim.2024.1353396 · Cited by 15
Hill, J. L., Grisnik, M., Hanscom, R. J., Sukumaran, J., Higham, T. E., & Clark, R. W. (2024) · Ecology and Evolution · doi:10.1002/ece3.11067 · Cited by 15
Irvine, P. J., Boucher, O., Kravitz, B., Alterskjær, K., Cole, J. N. S., Ji, D., Jones, A., Lunt, D. J., Moore, J. C., Muri, H., et al. (2014) · Journal of Geophysical Research Atmospheres · doi:10.1002/2013jd020716 · Cited by 16
Managing uncertainty in flood protection planning with climate projections
Dittes, B., Špačková, O., Schoppa, L., & Straub, D. (2018) · Hydrology and earth system sciences · doi:10.5194/hess-22-2511-2018 · Cited by 15
Boé, J., & Terray, L. (2015) · Climate Dynamics · doi:10.1007/s00382-014-2445-5 · Cited by 15
Zhang, M., Xu, Z., Han, Y., & Guo, W. (2021) · Geoscientific model development · doi:10.5194/gmd-14-3079-2021 · Cited by 15
Ring, C., Pollinger, F., Kaspar‐Ott, I., Hertig, E., Jacobeit, J., & Paeth, H. (2017) · Climate Dynamics · doi:10.1007/s00382-017-3737-3 · Cited by 14
Strobach, E., & Bel, G. (2020) · Nature Communications · doi:10.1038/s41467-020-14342-9 · Cited by 15
Changes in Future Precipitation Mean and Variability across Scales
Schwarzwald, K., Poppick, A., Rugenstein, M. A. A., Bloch‐Johnson, J., Wang, J., McInerney, D., & Moyer, E. J. (2020) · Journal of Climate · doi:10.1175/jcli-d-20-0001.1 · Cited by 14
Multiple Metrics Informed Projections of Future Precipitation in China
Wang, L., Qian, Y., Leung, L.-Y. R., Chen, X., Sarangi, C., Lu, J., Song, F., Gao, Y., Lin, G., & Zhang, Y. (2021) · Geophysical Research Letters · doi:10.1029/2021gl093810 · Cited by 14
Regional and elevational patterns of extreme heat stress change in the US
Raymond, C., Waliser, D. E., Guan, B., Lee, H., Loikith, P. C., Massoud, E., Sengupta, A., Singh, D., & Wootten, A. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac7343 · Cited by 14
Valcu‐Lisman, A. M., Gassman, P. W., Arritt, R. W., Campbell, T., & Herzmann, D. E. (2017) · Journal of Soil and Water Conservation · doi:10.2489/jswc.72.3.280 · Cited by 13
Sensitivity of winter North Atlantic-European climate to resolved atmosphere and ocean dynamics
Haarsma, R., García‐Serrano, J., Prodhomme, C., Bellprat, O., Davini, P., & Drijfhout, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-49865-9 · Cited by 14
Sensitivity of future climate change and uncertainty over India to performance-based model weighting
Singh, R., & AchutaRao, K. M. (2020) · Climatic Change · doi:10.1007/s10584-019-02643-y · Cited by 14
Projected changes in ENSO-driven regional tropical cyclone tracks
Bell, S. S., Chand, S. S., & Turville, C. (2020) · Climate Dynamics · doi:10.1007/s00382-020-05129-1 · Cited by 17
Method Uncertainty Is Essential for Reliable Confidence Statements of Precipitation Projections
Uhe, P. F., Mitchell, D., Bates, P., Allen, M. R., Betts, R., Huntingford, C., King, A. D., Sanderson, B. M., & Shiogama, H. (2020) · Journal of Climate · doi:10.1175/jcli-d-20-0289.1 · Cited by 15
Sixth Oregon climate assessment
Fleishman, E. (2023) · Oregon State University · doi:10.5399/osu/1161 · Cited by 16
Assessing observational constraints on future European climate in an out-of-sample framework
O’Reilly, C., Brunner, L., Qasmi, S., Nogherotto, R., Ballinger, A. P., Booth, B. B. B., Befort, D. J., Knutti, R., Schurer, A., Ribes, A., et al. (2024) · npj Climate and Atmospheric Science · doi:10.1038/s41612-024-00648-8 · Cited by 18
Self-citationKlimova, A., Gutiérrez‐Rivera, J. N., Ortega‐Rubio, A., & Eguiarte, L. E. (2024) · Ecology and Evolution · doi:10.1002/ece3.70027 · Cited by 13
Padua, V. M. N. D., & Ahn, K. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.132102 · Cited by 16
Ju, Q., Wu, J., Shen, T., Wang, Y., Cai, H., Jin, J., Jiang, P., Chen, X., & Du, Y. (2025) · Journal of Hydrology · doi:10.1016/j.jhydrol.2025.133945 · Cited by 16
Decision-Making by Water Managers Despite Climate Uncertainty
Wilby, R. L., & Murphy, C. (2019) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190455811.013.52 · Cited by 14
Plausible Drying and Wetting Scenarios for Summer in Southeastern South America
Mindlin, J., Vera, C. S., Shepherd, T. G., & Osman, M. (2023) · Journal of Climate · doi:10.1175/jcli-d-23-0134.1 · Cited by 13
Ogega, O. M., Gyampoh, B. A., & Mistry, M. (2020) · Climate · doi:10.3390/cli8120143 · Cited by 13
Jiang, Y., Kim, J. B., Still, C. J., Kerns, B. K., & Kline, J. D. (2018) · Scientific Data · doi:10.1038/sdata.2018.50 · Cited by 13
Cheng, L., & Liu, Z. (2022) · Earth s Future · doi:10.1029/2022ef002948 · Cited by 13
Ahn, K., de Padua, V. M. N., Kim, J. H., & Yi, J. (2023) · Journal of Hydrology · doi:10.1016/j.jhydrol.2023.130406 · Cited by 15
Climate Response to Vegetation Removal on Different Continents
Guo, J., Liu, Y., & Hu, Y. (2024) · Journal of Geophysical Research Atmospheres · doi:10.1029/2023jd039531 · Cited by 12
Future Projections of Heat Waves and Associated Mortality Risk in a Coastal Mediterranean City
Papadopoulos, G., Keppas, S. C., Parliari, D., Kontos, S., Papadogiannaki, S., & Melas, D. S. (2024) · Sustainability · doi:10.3390/su16031072 · Cited by 15
Adigun, P., Ogunrinde, A. T., Dairaku, K., Adebiyi, A. A., & Xian, X. (2024) · Solar Energy · doi:10.1016/j.solener.2024.113078 · Cited by 12
Tory, K. J., Ye, H., & Brunet, G. (2020) · Climate Dynamics · doi:10.1007/s00382-020-05440-x · Cited by 12
Ozone Variation Trends under Different CMIP6 Scenarios
Shang, L., Luo, J., & Wang, C. (2021) · Atmosphere · doi:10.3390/atmos12010112 · Cited by 12
The blessing of dimensionality for the analysis of climate data
Christiansen, B. (2021) · Nonlinear processes in geophysics · doi:10.5194/npg-28-409-2021 · Cited by 12
del Carmen, M. A. A., Estrada, F., & Gay-García, C. (2021) · Journal of Climate · doi:10.1175/jcli-d-20-0510.1 · Cited by 12
Feldman, D., Tadić, J. M., Arnold, W., & Schwarz, A. (2021) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001410 · Cited by 12
Risser, M. D., Collins, W. D., Wehner, M. F., O'Brien, T. A., Paciorek, C. J., O’Brien, J. P., Patricola, C. M., Huang, H., Ullrich, P. A., & Loring, B. (2022) · Climate Dynamics · doi:10.1007/s00382-022-06321-1 · Cited by 12
Maurer, C., Galmarini, S., Solazzo, E., Kusmierczyk-Michulec, J. T., Baré, J., Kalinowski, M., Schoeppner, M., Bourgouin, P., Crawford, A., Stein, A., et al. (2022) · Journal of Environmental Radioactivity · doi:10.1016/j.jenvrad.2022.106968 · Cited by 12
Southern Ocean Control of 2°C Global Warming in Climate Models
Shin, S., Yeh, S., An, S., Keenlyside, N. S., Xie, S., & Park, J. (2022) · Earth s Future · doi:10.1029/2022ef003212 · Cited by 12
Lei, Y., Peng, P., & Jiang, W. (2023) · Journal of Water and Climate Change · doi:10.2166/wcc.2023.464 · Cited by 12
Quantitative scenarios for future hydrologic extremes in the U.S. Southern Great Plains
Mullens, E. D., & McPherson, R. A. (2018) · International Journal of Climatology · doi:10.1002/joc.5979 · Cited by 12
Weak dependence of future global mean warming on the background climate state
Stolpe, M. B., Medhaug, I., Beyerle, U., & Knutti, R. (2019) · Climate Dynamics · doi:10.1007/s00382-019-04849-3 · Cited by 13
Self-citationLin, J., Zhou, C., Chen, L., Huang, G., Lamarque, J., Nie, J., Yang, J., Hu, K., Liu, P., Wang, J., et al. (2022) · Nature Geoscience · doi:10.1038/s41561-022-00898-2 · Cited by 12
‘Will the Paris Agreement protect us from hydro-meteorological extremes?’
Farinosi, F., Dosio, A., Calliari, E., Seliger, R., Alfieri, L., & Naumann, G. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/aba869 · Cited by 12
chooseGCM : A Toolkit to Select General Circulation Models in R
Esser, L. F., Bailly, D., Lima, M. R., & Ré, R. (2024) · Global Change Biology · doi:10.1111/gcb.70008 · Cited by 11
Downscaled CMIP6 future climate projections for New Zealand: climatology and extremes
Gibson, P. B., Broadbent, A. M., Stuart, S. J., Lewis, H., Campbell, I., Rampal, N., Harrington, L. J., & Williams, J. (2025) · Weather and Climate Extremes · doi:10.1016/j.wace.2025.100784 · Cited by 11
Maxim, A., & Grubert, E. A. (2021) · Environmental Research Infrastructure and Sustainability · doi:10.1088/2634-4505/ac33ef · Cited by 11
Climate-aware decision-making: lessons for electric grid infrastructure planning and operations
Brockway, A. M., Wang, L., Dunn, L. N., Callaway, D. S., & Jones, A. D. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac7815 · Cited by 13
An Integrated Modeling Framework in Projections of Hydrological Extremes
Meresa, H. K., Zhang, Y., Tian, J., Ma, N., Zhang, X., Heidari, H., & Naeem, S. (2022) · Surveys in Geophysics · doi:10.1007/s10712-022-09737-w · Cited by 12
Multi-model ensemble of frost risks across East Asia (1850–2100)
Richards, J., & Brimblecombe, P. (2024) · Climatic Change · doi:10.1007/s10584-024-03723-4 · Cited by 11
Grainger, S., Frederiksen, C. S., & Zheng, X. (2014) · Journal of Climate · doi:10.1175/jcli-d-14-00251.1 · Cited by 11
López-Novoa, U., Sáenz, J., Mendiburu, A., Miguel-Alonso, J., Errasti, I., Esnaola, G., Ezcurra, A., & Ibarra‐Berastegi, G. (2014) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2014.09.019 · Cited by 11
Tropical and Midlatitude Impact on Seasonal Polar Predictability in the Community Earth System Model
Blanchard‐Wrigglesworth, E., & Ding, Q. (2019) · Journal of Climate · doi:10.1175/jcli-d-19-0088.1 · Cited by 11
Etherington, T. R., Peltzer, D. A., & Wyse, S. V. (2022) · New Zealand Journal of Ecology · doi:10.20417/nzjecol.46.14 · Cited by 11
The xyz algorithm for fast interaction search in high-dimensional dataPreprint
Thanei, G.-A., Meinshausen, N., & Shah, R. D. (2016) · arXiv (Cornell University) · doi:10.17863/cam.43395 · Cited by 12
Calibrating large-ensemble European climate projections using observational data
O’Reilly, C., Befort, D. J., & Weisheimer, A. (2020) · Earth System Dynamics · doi:10.5194/esd-11-1033-2020 · Cited by 13
Bhowmik, R. D., & Sankarasubramanian, A. (2020) · International Journal of Climatology · doi:10.1002/joc.6870 · Cited by 12
Baiamonte, G., Gristina, L., Minacapilli, M., & Novara, A. (2021) · CATENA · doi:10.1016/j.catena.2021.105350 · Cited by 10
Dupont-Doaré, C., & Alagador, D. (2021) · Biological Conservation · doi:10.1016/j.biocon.2021.109330 · Cited by 11
Pryor, J. W., Zhang, Q., & Arias, M. E. (2021) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001502 · Cited by 10
The Impact of Winds on AMOC in a Fully‐Coupled Climate Model
Roach, L. A., Blanchard‐Wrigglesworth, E., Ragen, S., Cheng, W., Armour, K. C., & Bitz, C. M. (2022) · Geophysical Research Letters · doi:10.1029/2022gl101203 · Cited by 10
Yalla, V. S., Sriram, V., & Murali, K. (2023) · Natural Hazards · doi:10.1007/s11069-023-06244-y · Cited by 10
Addor, N., Ewen, T. L., Johnson, L., Çöltekin, A., Derungs, C., & Muccione, V. (2015) · Earth s Future · doi:10.1002/2015ef000303 · Cited by 10
Indices of climate change based on patterns from CMIP5 models, and the range of projections
Watterson, I. G. (2018) · Climate Dynamics · doi:10.1007/s00382-018-4260-x · Cited by 10
Zhou, C., Liu, P., Huang, G., Lin, J., Hu, K., Chen, L., Wang, J., Li, S., Wang, S., & Ni, R. (2020) · Theoretical and Applied Climatology · doi:10.1007/s00704-020-03249-6 · Cited by 10
Northern Hemisphere winter midlatitude atmospheric variability in CMIP5 models
Biagio, V. D., Calmanti, S., Dell’Aquila, A., & Ruti, P. M. (2014) · Geophysical Research Letters · doi:10.1002/2013gl058928 · Cited by 10
Next-Generation Drought Forecasting: Hybrid AI Models for Climate Resilience
Liu, J., Tie, L., Huang, L., Ren, Y., & He, P. (2025) · Remote Sensing · doi:10.3390/rs17203402 · Cited by 12
Pacchetti, M. B., Dessai, S., Stainforth, D. A., & Bradley, S. (2021) · Climatic Change · doi:10.1007/s10584-021-03187-w · Cited by 9
Ensembles for Viticulture Climate Classifications of the Willamette Valley Wine Region
Skahill, B., Berenguer, B., & Stoll, M. (2021) · Climate · doi:10.3390/cli9090140 · Cited by 9
Scenarios of change in the realized climatic niche of mountain carnivores and ungulates
Dragonetti, C., Angarita, V. Y. M., & Marco, M. D. (2022) · Conservation Biology · doi:10.1111/cobi.14035 · Cited by 10
Indian summer monsoon simulations in successive generations of the NCAR Community Atmosphere Model
Kumar, R., Pathak, R., Sahany, S., & Mishra, S. K. (2023) · Theoretical and Applied Climatology · doi:10.1007/s00704-023-04514-0 · Cited by 9
Wang, H., & Asefa, T. (2024) · International Journal of Climatology · doi:10.1002/joc.8479 · Cited by 9
Olson, R., An, S., Fan, Y., Chang, W., Evans, J. P., & Lee, J. (2019) · Nature Communications · doi:10.1038/s41467-019-10561-x · Cited by 9
Shrestha, S. G., & Pradhanang, S. M. (2022) · Theoretical and Applied Climatology · doi:10.1007/s00704-022-04073-w · Cited by 9
Managing climate change scenarios for societal impact studies
Carbone, G. J. (2014) · Physical Geography · doi:10.1080/02723646.2013.869714 · Cited by 9
On judging the credibility of climate predictions
Otto, F. E. L., Ferro, C. A. T., Fricker, T. E., & Suckling, E. B. (2013) · Climatic Change · doi:10.1007/s10584-013-0813-5 · Cited by 9
A climate generator for agricultural planning in southeastern South America
Greene, A. M., Goddard, L., González, P., Ines, A. V. M., & Chryssanthacopoulos, J. P. (2015) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2015.01.008 · Cited by 9
Mekhaimr, S. A., & Wahab, M. A. (2019) · Atmospheric Pollution Research · doi:10.1016/j.apr.2019.03.008 · Cited by 9
Wohland, J., Hoffmann, P., Lima, D. C. A., Breil, M., Asselin, O., & Rechid, D. (2024) · Earth System Dynamics · doi:10.5194/esd-15-1385-2024 · Cited by 8
Alaska Daily Extreme Precipitation Processes in a Subset of CMIP5 Global Climate Models
Smalley, K. M., Glisan, J. M., & Gutowski, W. J. (2019) · Journal of Geophysical Research Atmospheres · doi:10.1029/2018jd028643 · Cited by 8
Lutsko, N. J., Luongo, M. T., Wall, C. J., & Myers, T. A. (2022) · Journal of Geophysical Research Atmospheres · doi:10.1029/2022jd037486 · Cited by 8
Sutton, W. B., Grisnik, M., Williams, L. A., & Groves, J. D. (2023) · Global Ecology and Conservation · doi:10.1016/j.gecco.2023.e02554 · Cited by 8
Bouska, K. L., Whitledge, G. W., Lant, C. L., & Schoof, J. T. (2018) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/cjfas-2018-0002 · Cited by 8
Voskamp, A., Butchart, S. H. M., Baker, D. J., Wilsey, C. B., & Willis, S. G. (2021) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.625432 · Cited by 10
Comparing climate time series – Part 2: A multivariate test
DelSole, T., & Tippett, M. K. (2021) · Advances in statistical climatology, meteorology and oceanography · doi:10.5194/ascmo-7-73-2021 · Cited by 8
Investigation of Historical and Future Air Temperature Changes in the UAE
Hassan, R. N., Zeiada, W., Abuzwidah, M., Mirou, S. M., & Ashour, A. G. (2022) · Sustainable civil infrastructures · doi:10.1007/978-3-030-79801-7_81 · Cited by 8
Identifying climate models based on their daily output using machine learning
Brunner, L., & Sippel, S. (2023) · Environmental Data Science · doi:10.1017/eds.2023.23 · Cited by 9
Schurer, A., Hegerl, G. C., Goosse, H., Bollasina, M. A., England, M. H., Mineter, M. J., Smith, D. M., & Tett, S. F. B. (2023) · Climate of the past · doi:10.5194/cp-19-943-2023 · Cited by 9
Metadata
- Authors
- Knutti, R., Masson, D., & Gettelman, A.
- Affiliations
- ETH Zurich (CH)
- Federal Office of Meteorology and Climatology MeteoSwiss (CH)
- NSF National Center for Atmospheric Research (US)
- Corresponding author
- Knutti, Reto
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2013-03-26
- Volume
- 40
- Issue
- 6
- Pages
- 1194-1199
- ISSN
- 0094-8276 (ISSN-L)
- 1944-8007
- OpenAlex
- W1796961055
- Semantic Scholar
- f6507ca951e61214c9268ce9a43376724b2f9bcf
- Cited by
- 918
- References
- 17
- Citations by year
- 2026: 37 · 2025: 42 · 2024: 54 · 2023: 73 · 2022: 114 · 2021: 126 · 2020: 109 · 2019: 64 · 2018: 76 · 2017: 67 · 2016: 51 · 2015: 55 · 2014: 42 · 2013: 7
- FWCI
- 39.41
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Geophysical Research Letters (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Keywords
- CMIP5, multi-model ensemble, model validation against observations, climate change projections, statistical model comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review